Understanding causality is key to the success of NLP applications, especially
in high-stakes domains. Causality comes in various perspectives such as enable
and prevent that, despite their importance, have been largely ignored in the
literature. This paper introduces a novel fine-grained causal reasoning dataset
and presents a series of novel predictive tasks in NLP, such as causality
detection, event causality extraction, and Causal QA. Our dataset contains
human annotations of 25K cause-effect event pairs and 24K question-answering
pairs within multi-sentence samples, where each can have multiple causal
relationships. Through extensive experiments and analysis, we show that the
complex relations in our dataset bring unique challenges to state-of-the-art
methods across all three tasks and highlight potential research opportunities,
especially in developing "causal-thinking" methods.

本文介绍了一种新的精细化因果推理数据集，并在自然语言处理中提出了一系列新的预测任务，例如因果检测、事件因果提取和因果问答。作者通过大量实验和分析表明，数据集中的复杂关系对最先进的方法带来了独特的挑战，并强调了潜在的研究机会，特别是在发展 "因果思维" 方法方面。